Application of an Adaptive Gaussian Mixture Regression for Fault Detection and Diagnosis across various HVAC Systems

This study explores the application of an adaptive Gaussian Mixture Regression (GMR) framework for fault detection and diagnosis (FDD) across three distinct HVAC systems: passive chilled beams (PCB), radiant slabs (RS), and conventional Variable Air Volume (VAV) systems. This investigation addresses the growing need for robust and adaptable fault detection methods capable of maintaining high diagnostic precision across various HVAC configurations, which exhibit unique operational characteristics and fault signatures.

The methodology follows three crucial steps: system modeling, feature selection, and FDD. First, the GMR model is used to predict the flow and the heating and cooling capacities of each HVAC system. Accurate prediction of these parameters is crucial, as they are important features during the FDD process. Next, a feature selection algorithm is implemented to identify the optimal features for fault diagnosis for each HVAC system. The selected features are then compared across the three systems to identify similarities and differences. Understanding these similarities is valuable for developing generalized fault detection strategies that can be adapted to multiple HVAC configurations, thereby improving the scalability and versatility of the FDD framework. The performance of the FDD model is evaluated by testing it against various fault scenarios in each HVAC system in the presence of both known and unknown faults. The evolving nature of the GMR model allows it to update its parameters and integrate new Gaussian components when encountering previously unknown states, thus maintaining high diagnostic accuracy over time.

The feature selection process highlights both common and unique features across the HVAC systems. Common features include variables related to flow rates and heating/cooling capacity of the system which are critical for diagnosing faults in any HVAC system. Unique features, specific to each system’s operational principles, further enhance the model’s diagnostic precision. For instance, slab surface temperature is critical for radiant slabs, while supply air temperature deviation from the setpoint is more significant for the VAV system. In terms of fault prediction accuracy, the GMR model shows remarkable effectiveness across all three HVAC systems. The fault prediction accuracy was above 90% for all three systems, with the highest performance observed in the radiant slabs, followed by the passive chilled beams and the VAV system. The evolving capability of the GMR model plays a significant role in maintaining this high accuracy, as it allows the model to adapt to new fault patterns and operational changes over time.